km_curv_creator.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KM_Curve_Practice"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mstring\u001b[39;00m \u001b[39mimport\u001b[39;00m ascii_lowercase\n\u001b[1;32m      5\u001b[0m \u001b[39m# Visualisation\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[1;32m      8\u001b[0m sns\u001b[39m.\u001b[39mset(style\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mdark\u001b[39m\u001b[39m'\u001b[39m, context\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mtalk\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "# Data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from string import ascii_lowercase\n",
    "# Visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style='dark', context='talk')\n",
    "# Kaplan-Meier curve\n",
    "from lifelines import KaplanMeierFitter \n",
    "from sksurv.nonparametric import kaplan_meier_estimator\n",
    "# Create a toy dataframe\n",
    "df = pd.DataFrame(data={'duration': [3,9,5,8,7,2,1,3,4],\n",
    "                        'event': [1,0,1,0,1,0,1,0,0]}, \n",
    "                  index=list(ascii_lowercase[:9]))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 7 8 9]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4235/2283970506.py:7: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[0, df.shape[0], 0, 1]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n",
      "/tmp/ipykernel_4235/2283970506.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  km = km.append(pd.DataFrame([[t, n, d, s]],\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>n_at_risk</th>\n",
       "      <th>n_events</th>\n",
       "      <th>survival_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  n_at_risk  n_events  survival_probability\n",
       "0       0.0        9.0       0.0              1.000000\n",
       "1       1.0        9.0       1.0              0.888889\n",
       "2       2.0        8.0       0.0              0.888889\n",
       "3       3.0        7.0       1.0              0.761905\n",
       "4       4.0        5.0       0.0              0.761905\n",
       "5       5.0        4.0       1.0              0.571429\n",
       "6       7.0        3.0       1.0              0.380952\n",
       "7       8.0        2.0       0.0              0.380952\n",
       "8       9.0        1.0       0.0              0.380952"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "durations = df.sort_values('duration')['duration'].unique()\n",
    "# Initialise the table\n",
    "print(durations)\n",
    "columns = ['duration', 'n_at_risk', 'n_events', \n",
    "           'survival_probability']\n",
    "km = pd.DataFrame(columns=columns, dtype=np.number)\n",
    "km = km.append(pd.DataFrame([[0, df.shape[0], 0, 1]], \n",
    "                            columns=columns))\n",
    "# Calculate survival probability for each duration\n",
    "for i, t in enumerate(durations):\n",
    "    n = np.sum(df['duration']>=t)\n",
    "    d = np.sum((df['duration']==t) & (df['event']==1))\n",
    "    s = (1 - d / n) * km.loc[i, 'survival_probability']\n",
    "    km = km.append(pd.DataFrame([[t, n, d, s]], \n",
    "                                index=[i+1],\n",
    "                                columns=columns))\n",
    "km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.lineplot(data=km, x='duration', y='survival_probability', \n",
    "             drawstyle='steps-negt')\n",
    "plt.ylim(0,1.1) \n",
    "plt.title(\"Kaplan-Meier curve\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf = KaplanMeierFitter() \n",
    "kmf.fit(df['duration'], df['event'])\n",
    "plt.figure(figsize=(8,4))\n",
    "kmf.plot()\n",
    "plt.title(\"Kaplan-Meier curve\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration, survival_probability = kaplan_meier_estimator(df['event']==1, df['duration'])\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.step(x=duration, y=survival_probability, where=\"negt\")\n",
    "plt.ylim(0,1.1)\n",
    "plt.xlabel(\"Survival time (months)\")\n",
    "plt.ylabel(\"Survival probability\")\n",
    "plt.title(\"Kaplan-Meier curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "survival_data = pd.read_csv(\"TCGA-BRCA_clinical.csv\")\n",
    "survival_data.set_index(survival_data['SAMPLE_ID'] , inplace = True)\n",
    "survival_data.drop(columns = [\"SAMPLE_ID\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "numDaysInMonth = 30.437\n",
    "survival_data[\"OS_DAYS\"] = survival_data[\"OS_MONTHS\"] * 30.437\n",
    "survival_data.sort_values([\"OS_DAYS\"], inplace = True)\n",
    "test_df = survival_data[[\"OS_DAYS\", \"OS_STATUS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_ID</th>\n",
       "      <th>ONCOTREE_CODE</th>\n",
       "      <th>CANCER_TYPE</th>\n",
       "      <th>CANCER_TYPE_DETAILED</th>\n",
       "      <th>TUMOR_TYPE</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>TISSUE_PROSPECTIVE_COLLECTION_INDICATOR</th>\n",
       "      <th>TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR</th>\n",
       "      <th>TISSUE_SOURCE_SITE_CODE</th>\n",
       "      <th>TUMOR_TISSUE_SITE</th>\n",
       "      <th>...</th>\n",
       "      <th>OS_STATUS</th>\n",
       "      <th>OS_MONTHS</th>\n",
       "      <th>DSS_STATUS</th>\n",
       "      <th>DSS_MONTHS</th>\n",
       "      <th>DFS_STATUS</th>\n",
       "      <th>DFS_MONTHS</th>\n",
       "      <th>PFS_STATUS</th>\n",
       "      <th>PFS_MONTHS</th>\n",
       "      <th>OS_DAYS</th>\n",
       "      <th>isMPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMPLE_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A08H-01</th>\n",
       "      <td>TCGA-A8-A08H</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A094-01</th>\n",
       "      <td>TCGA-A8-A094</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A090-01</th>\n",
       "      <td>TCGA-A8-A090</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-C8-A12K-01</th>\n",
       "      <td>TCGA-C8-A12K</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>C8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A09Z-01</th>\n",
       "      <td>TCGA-A8-A09Z</td>\n",
       "      <td>ILC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Lobular Carcinoma</td>\n",
       "      <td>Infiltrating Lobular Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0RE-01</th>\n",
       "      <td>TCGA-B6-A0RE</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>255.679390</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>255.679390</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>255.679390</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>255.679390</td>\n",
       "      <td>7782.113587</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0RN-01</th>\n",
       "      <td>TCGA-B6-A0RN</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>8013.265475</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0IA-01</th>\n",
       "      <td>TCGA-B6-A0IA</td>\n",
       "      <td>BRCNOS</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Carcinoma (NOS)</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>8396.517310</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0I5-01</th>\n",
       "      <td>TCGA-B6-A0I5</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>8561.625803</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0RU-01</th>\n",
       "      <td>TCGA-B6-A0RU</td>\n",
       "      <td>BRCNOS</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Carcinoma (NOS)</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>282.901009</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>282.901009</td>\n",
       "      <td>1:Recurred/Progressed</td>\n",
       "      <td>101.127659</td>\n",
       "      <td>1:PROGRESSION</td>\n",
       "      <td>101.127659</td>\n",
       "      <td>8610.658020</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PATIENT_ID ONCOTREE_CODE    CANCER_TYPE  \\\n",
       "SAMPLE_ID                                                    \n",
       "TCGA-A8-A08H-01  TCGA-A8-A08H           IDC  Breast Cancer   \n",
       "TCGA-A8-A094-01  TCGA-A8-A094           IDC  Breast Cancer   \n",
       "TCGA-A8-A090-01  TCGA-A8-A090           IDC  Breast Cancer   \n",
       "TCGA-C8-A12K-01  TCGA-C8-A12K           IDC  Breast Cancer   \n",
       "TCGA-A8-A09Z-01  TCGA-A8-A09Z           ILC  Breast Cancer   \n",
       "...                       ...           ...            ...   \n",
       "TCGA-B6-A0RE-01  TCGA-B6-A0RE           IDC  Breast Cancer   \n",
       "TCGA-B6-A0RN-01  TCGA-B6-A0RN           IDC  Breast Cancer   \n",
       "TCGA-B6-A0IA-01  TCGA-B6-A0IA        BRCNOS  Breast Cancer   \n",
       "TCGA-B6-A0I5-01  TCGA-B6-A0I5           IDC  Breast Cancer   \n",
       "TCGA-B6-A0RU-01  TCGA-B6-A0RU        BRCNOS  Breast Cancer   \n",
       "\n",
       "                              CANCER_TYPE_DETAILED  \\\n",
       "SAMPLE_ID                                            \n",
       "TCGA-A8-A08H-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-A8-A094-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-A8-A090-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-C8-A12K-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-A8-A09Z-01  Breast Invasive Lobular Carcinoma   \n",
       "...                                            ...   \n",
       "TCGA-B6-A0RE-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-B6-A0RN-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-B6-A0IA-01    Breast Invasive Carcinoma (NOS)   \n",
       "TCGA-B6-A0I5-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-B6-A0RU-01    Breast Invasive Carcinoma (NOS)   \n",
       "\n",
       "                                     TUMOR_TYPE  GRADE  \\\n",
       "SAMPLE_ID                                                \n",
       "TCGA-A8-A08H-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-A8-A094-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-A8-A090-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-C8-A12K-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-A8-A09Z-01  Infiltrating Lobular Carcinoma    NaN   \n",
       "...                                         ...    ...   \n",
       "TCGA-B6-A0RE-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-B6-A0RN-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-B6-A0IA-01                           Other    NaN   \n",
       "TCGA-B6-A0I5-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-B6-A0RU-01                           Other    NaN   \n",
       "\n",
       "                TISSUE_PROSPECTIVE_COLLECTION_INDICATOR  \\\n",
       "SAMPLE_ID                                                 \n",
       "TCGA-A8-A08H-01                                      No   \n",
       "TCGA-A8-A094-01                                      No   \n",
       "TCGA-A8-A090-01                                      No   \n",
       "TCGA-C8-A12K-01                                     Yes   \n",
       "TCGA-A8-A09Z-01                                      No   \n",
       "...                                                 ...   \n",
       "TCGA-B6-A0RE-01                                      No   \n",
       "TCGA-B6-A0RN-01                                      No   \n",
       "TCGA-B6-A0IA-01                                      No   \n",
       "TCGA-B6-A0I5-01                                      No   \n",
       "TCGA-B6-A0RU-01                                      No   \n",
       "\n",
       "                TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR  \\\n",
       "SAMPLE_ID                                                   \n",
       "TCGA-A8-A08H-01                                       Yes   \n",
       "TCGA-A8-A094-01                                       Yes   \n",
       "TCGA-A8-A090-01                                       Yes   \n",
       "TCGA-C8-A12K-01                                        No   \n",
       "TCGA-A8-A09Z-01                                       Yes   \n",
       "...                                                   ...   \n",
       "TCGA-B6-A0RE-01                                       Yes   \n",
       "TCGA-B6-A0RN-01                                       Yes   \n",
       "TCGA-B6-A0IA-01                                       Yes   \n",
       "TCGA-B6-A0I5-01                                       Yes   \n",
       "TCGA-B6-A0RU-01                                       Yes   \n",
       "\n",
       "                TISSUE_SOURCE_SITE_CODE TUMOR_TISSUE_SITE  ...  OS_STATUS  \\\n",
       "SAMPLE_ID                                                  ...              \n",
       "TCGA-A8-A08H-01                      A8            Breast  ...   0:LIVING   \n",
       "TCGA-A8-A094-01                      A8            Breast  ...   0:LIVING   \n",
       "TCGA-A8-A090-01                      A8            Breast  ...   0:LIVING   \n",
       "TCGA-C8-A12K-01                      C8            Breast  ...   0:LIVING   \n",
       "TCGA-A8-A09Z-01                      A8            Breast  ...   0:LIVING   \n",
       "...                                 ...               ...  ...        ...   \n",
       "TCGA-B6-A0RE-01                      B6            Breast  ...   0:LIVING   \n",
       "TCGA-B6-A0RN-01                      B6            Breast  ...   0:LIVING   \n",
       "TCGA-B6-A0IA-01                      B6            Breast  ...   0:LIVING   \n",
       "TCGA-B6-A0I5-01                      B6            Breast  ...   0:LIVING   \n",
       "TCGA-B6-A0RU-01                      B6            Breast  ...   0:LIVING   \n",
       "\n",
       "                  OS_MONTHS                  DSS_STATUS  DSS_MONTHS  \\\n",
       "SAMPLE_ID                                                             \n",
       "TCGA-A8-A08H-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A8-A094-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A8-A090-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-C8-A12K-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A8-A09Z-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "...                     ...                         ...         ...   \n",
       "TCGA-B6-A0RE-01  255.679390  0:ALIVE OR DEAD TUMOR FREE  255.679390   \n",
       "TCGA-B6-A0RN-01  263.273827  0:ALIVE OR DEAD TUMOR FREE  263.273827   \n",
       "TCGA-B6-A0IA-01  275.865470  0:ALIVE OR DEAD TUMOR FREE  275.865470   \n",
       "TCGA-B6-A0I5-01  281.290068  0:ALIVE OR DEAD TUMOR FREE  281.290068   \n",
       "TCGA-B6-A0RU-01  282.901009  0:ALIVE OR DEAD TUMOR FREE  282.901009   \n",
       "\n",
       "                            DFS_STATUS  DFS_MONTHS     PFS_STATUS  PFS_MONTHS  \\\n",
       "SAMPLE_ID                                                                       \n",
       "TCGA-A8-A08H-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "TCGA-A8-A094-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "TCGA-A8-A090-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "TCGA-C8-A12K-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "TCGA-A8-A09Z-01                    NaN         NaN     0:CENSORED    0.000000   \n",
       "...                                ...         ...            ...         ...   \n",
       "TCGA-B6-A0RE-01          0:DiseaseFree  255.679390     0:CENSORED  255.679390   \n",
       "TCGA-B6-A0RN-01          0:DiseaseFree  263.273827     0:CENSORED  263.273827   \n",
       "TCGA-B6-A0IA-01          0:DiseaseFree  275.865470     0:CENSORED  275.865470   \n",
       "TCGA-B6-A0I5-01          0:DiseaseFree  281.290068     0:CENSORED  281.290068   \n",
       "TCGA-B6-A0RU-01  1:Recurred/Progressed  101.127659  1:PROGRESSION  101.127659   \n",
       "\n",
       "                     OS_DAYS  isMPS  \n",
       "SAMPLE_ID                            \n",
       "TCGA-A8-A08H-01     0.000000  False  \n",
       "TCGA-A8-A094-01     0.000000  False  \n",
       "TCGA-A8-A090-01     0.000000  False  \n",
       "TCGA-C8-A12K-01     0.000000  False  \n",
       "TCGA-A8-A09Z-01     0.000000  False  \n",
       "...                      ...    ...  \n",
       "TCGA-B6-A0RE-01  7782.113587  False  \n",
       "TCGA-B6-A0RN-01  8013.265475  False  \n",
       "TCGA-B6-A0IA-01  8396.517310  False  \n",
       "TCGA-B6-A0I5-01  8561.625803  False  \n",
       "TCGA-B6-A0RU-01  8610.658020  False  \n",
       "\n",
       "[1082 rows x 55 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmf = KaplanMeierFitter()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4235/3270634811.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['OS_STATUS'].replace(\"0:LIVING\",0, inplace = True)\n",
      "/tmp/ipykernel_4235/3270634811.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_df['OS_STATUS'].replace(\"1:DECEASED\", 1, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "test_df['OS_STATUS'].replace(\"0:LIVING\",0, inplace = True)\n",
    "test_df['OS_STATUS'].replace(\"1:DECEASED\", 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.KaplanMeierFitter:\"KM_estimate\", fitted with 1082 total observations, 931 right-censored observations>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmf.fit(durations = test_df['OS_DAYS'], event_observed = test_df['OS_STATUS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timeline'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmf.plot(show_censors = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now need to compare survival curves of patients that are MPS+ and MPS-\n",
    "MPS_Scores = pd.read_csv(\"MPSScores_7.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "survival_data['isMPS'] = False\n",
    "# need to create a filter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>Patient_Names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.715959</td>\n",
       "      <td>TCGA-3C-AAAU-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>TCGA-3C-AALI-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>TCGA-3C-AALJ-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.070766</td>\n",
       "      <td>TCGA-3C-AALK-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.307485</td>\n",
       "      <td>TCGA-4H-AAAK-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>TCGA-WT-AB44-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>TCGA-XX-A899-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>0.608043</td>\n",
       "      <td>TCGA-XX-A89A-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>2.744273</td>\n",
       "      <td>TCGA-Z7-A8R5-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>-0.285366</td>\n",
       "      <td>TCGA-Z7-A8R6-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1082 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0    Patient_Names\n",
       "0    -1.715959  TCGA-3C-AAAU-01\n",
       "1    -0.000000  TCGA-3C-AALI-01\n",
       "2    -0.000000  TCGA-3C-AALJ-01\n",
       "3     0.070766  TCGA-3C-AALK-01\n",
       "4    -0.307485  TCGA-4H-AAAK-01\n",
       "...        ...              ...\n",
       "1077  0.000000  TCGA-WT-AB44-01\n",
       "1078  0.000000  TCGA-XX-A899-01\n",
       "1079  0.608043  TCGA-XX-A89A-01\n",
       "1080  2.744273  TCGA-Z7-A8R5-01\n",
       "1081 -0.285366  TCGA-Z7-A8R6-01\n",
       "\n",
       "[1082 rows x 2 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MPS_Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "MPS_Scores = pd.DataFrame(MPS_Scores)\n",
    "MPS_Scores_Positive = MPS_Scores[MPS_Scores['0'].apply(lambda x : x > 0)]\n",
    "MPS_Scores_Negative = MPS_Scores[MPS_Scores['0'].apply(lambda x : x < 0)]\n",
    "patient_IDs_positive = MPS_Scores_Positive['Patient_Names'].tolist()\n",
    "patient_IDs_negative = MPS_Scores_Negative['Patient_Names'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "survival_data_2_pos = survival_data[survival_data.index.isin(patient_IDs_positive)]\n",
    "survival_data_2_neg = survival_data[survival_data.index.isin(patient_IDs_negative)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4235/398321379.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survival_data_2_pos['OS_STATUS'].replace(\"0:LIVING\",0, inplace = True)\n",
      "/tmp/ipykernel_4235/398321379.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survival_data_2_pos['OS_STATUS'].replace(\"1:DECEASED\", 1, inplace = True)\n",
      "/tmp/ipykernel_4235/398321379.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survival_data_2_neg['OS_STATUS'].replace(\"0:LIVING\",0, inplace = True)\n",
      "/tmp/ipykernel_4235/398321379.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  survival_data_2_neg['OS_STATUS'].replace(\"1:DECEASED\", 1, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "survival_data_2_pos['OS_STATUS'].replace(\"0:LIVING\",0, inplace = True)\n",
    "survival_data_2_pos['OS_STATUS'].replace(\"1:DECEASED\", 1, inplace = True)\n",
    "\n",
    "survival_data_2_neg['OS_STATUS'].replace(\"0:LIVING\",0, inplace = True)\n",
    "survival_data_2_neg['OS_STATUS'].replace(\"1:DECEASED\", 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "print(0 < -0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_ID</th>\n",
       "      <th>ONCOTREE_CODE</th>\n",
       "      <th>CANCER_TYPE</th>\n",
       "      <th>CANCER_TYPE_DETAILED</th>\n",
       "      <th>TUMOR_TYPE</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>TISSUE_PROSPECTIVE_COLLECTION_INDICATOR</th>\n",
       "      <th>TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR</th>\n",
       "      <th>TISSUE_SOURCE_SITE_CODE</th>\n",
       "      <th>TUMOR_TISSUE_SITE</th>\n",
       "      <th>...</th>\n",
       "      <th>OS_STATUS</th>\n",
       "      <th>OS_MONTHS</th>\n",
       "      <th>DSS_STATUS</th>\n",
       "      <th>DSS_MONTHS</th>\n",
       "      <th>DFS_STATUS</th>\n",
       "      <th>DFS_MONTHS</th>\n",
       "      <th>PFS_STATUS</th>\n",
       "      <th>PFS_MONTHS</th>\n",
       "      <th>OS_DAYS</th>\n",
       "      <th>isMPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMPLE_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A08H-01</th>\n",
       "      <td>TCGA-A8-A08H</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A7-A0CD-01</th>\n",
       "      <td>TCGA-A7-A0CD</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>A7</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>38.300950</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>38.300950</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>38.300950</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>38.300950</td>\n",
       "      <td>1165.766019</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-E9-A22B-01</th>\n",
       "      <td>TCGA-E9-A22B</td>\n",
       "      <td>BRCNOS</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Carcinoma (NOS)</td>\n",
       "      <td>Mixed Histology (NOS)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>E9</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>38.366703</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>38.366703</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>38.366703</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>38.366703</td>\n",
       "      <td>1167.767334</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-AR-A5QP-01</th>\n",
       "      <td>TCGA-AR-A5QP</td>\n",
       "      <td>ILC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Lobular Carcinoma</td>\n",
       "      <td>Infiltrating Lobular Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AR</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>38.958477</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>38.958477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>38.958477</td>\n",
       "      <td>1185.779170</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A1-A0SN-01</th>\n",
       "      <td>TCGA-A1-A0SN</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A1</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>39.320117</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>39.320117</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>39.320117</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>39.320117</td>\n",
       "      <td>1196.786402</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-EW-A1P8-01</th>\n",
       "      <td>TCGA-EW-A1P8</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>EW</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>7.857448</td>\n",
       "      <td>1:DEAD WITH TUMOR</td>\n",
       "      <td>7.857448</td>\n",
       "      <td>1:Recurred/Progressed</td>\n",
       "      <td>6.805405</td>\n",
       "      <td>1:PROGRESSION</td>\n",
       "      <td>6.805405</td>\n",
       "      <td>239.157149</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-BH-A1F5-01</th>\n",
       "      <td>TCGA-BH-A1F5</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>BH</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>89.160667</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>89.160667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>89.160667</td>\n",
       "      <td>2713.783213</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-LL-A73Z-01</th>\n",
       "      <td>TCGA-LL-A73Z</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>LL</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>7.462932</td>\n",
       "      <td>1:DEAD WITH TUMOR</td>\n",
       "      <td>7.462932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1:PROGRESSION</td>\n",
       "      <td>6.312260</td>\n",
       "      <td>227.149259</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-AC-A2FE-01</th>\n",
       "      <td>TCGA-AC-A2FE</td>\n",
       "      <td>ILC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Lobular Carcinoma</td>\n",
       "      <td>Infiltrating Lobular Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>AC</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>86.662064</td>\n",
       "      <td>1:DEAD WITH TUMOR</td>\n",
       "      <td>86.662064</td>\n",
       "      <td>1:Recurred/Progressed</td>\n",
       "      <td>25.183286</td>\n",
       "      <td>1:PROGRESSION</td>\n",
       "      <td>25.183286</td>\n",
       "      <td>2637.733241</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0WS-01</th>\n",
       "      <td>TCGA-B6-A0WS</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>97.478384</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>97.478384</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>97.478384</td>\n",
       "      <td>2966.949568</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>824 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PATIENT_ID ONCOTREE_CODE    CANCER_TYPE  \\\n",
       "SAMPLE_ID                                                    \n",
       "TCGA-A8-A08H-01  TCGA-A8-A08H           IDC  Breast Cancer   \n",
       "TCGA-A7-A0CD-01  TCGA-A7-A0CD           IDC  Breast Cancer   \n",
       "TCGA-E9-A22B-01  TCGA-E9-A22B        BRCNOS  Breast Cancer   \n",
       "TCGA-AR-A5QP-01  TCGA-AR-A5QP           ILC  Breast Cancer   \n",
       "TCGA-A1-A0SN-01  TCGA-A1-A0SN           IDC  Breast Cancer   \n",
       "...                       ...           ...            ...   \n",
       "TCGA-EW-A1P8-01  TCGA-EW-A1P8           IDC  Breast Cancer   \n",
       "TCGA-BH-A1F5-01  TCGA-BH-A1F5           IDC  Breast Cancer   \n",
       "TCGA-LL-A73Z-01  TCGA-LL-A73Z           IDC  Breast Cancer   \n",
       "TCGA-AC-A2FE-01  TCGA-AC-A2FE           ILC  Breast Cancer   \n",
       "TCGA-B6-A0WS-01  TCGA-B6-A0WS           IDC  Breast Cancer   \n",
       "\n",
       "                              CANCER_TYPE_DETAILED  \\\n",
       "SAMPLE_ID                                            \n",
       "TCGA-A8-A08H-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-A7-A0CD-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-E9-A22B-01    Breast Invasive Carcinoma (NOS)   \n",
       "TCGA-AR-A5QP-01  Breast Invasive Lobular Carcinoma   \n",
       "TCGA-A1-A0SN-01   Breast Invasive Ductal Carcinoma   \n",
       "...                                            ...   \n",
       "TCGA-EW-A1P8-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-BH-A1F5-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-LL-A73Z-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-AC-A2FE-01  Breast Invasive Lobular Carcinoma   \n",
       "TCGA-B6-A0WS-01   Breast Invasive Ductal Carcinoma   \n",
       "\n",
       "                                     TUMOR_TYPE  GRADE  \\\n",
       "SAMPLE_ID                                                \n",
       "TCGA-A8-A08H-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-A7-A0CD-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-E9-A22B-01           Mixed Histology (NOS)    NaN   \n",
       "TCGA-AR-A5QP-01  Infiltrating Lobular Carcinoma    NaN   \n",
       "TCGA-A1-A0SN-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "...                                         ...    ...   \n",
       "TCGA-EW-A1P8-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-BH-A1F5-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-LL-A73Z-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-AC-A2FE-01  Infiltrating Lobular Carcinoma    NaN   \n",
       "TCGA-B6-A0WS-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "\n",
       "                TISSUE_PROSPECTIVE_COLLECTION_INDICATOR  \\\n",
       "SAMPLE_ID                                                 \n",
       "TCGA-A8-A08H-01                                      No   \n",
       "TCGA-A7-A0CD-01                                     Yes   \n",
       "TCGA-E9-A22B-01                                     Yes   \n",
       "TCGA-AR-A5QP-01                                      No   \n",
       "TCGA-A1-A0SN-01                                      No   \n",
       "...                                                 ...   \n",
       "TCGA-EW-A1P8-01                                      No   \n",
       "TCGA-BH-A1F5-01                                      No   \n",
       "TCGA-LL-A73Z-01                                     Yes   \n",
       "TCGA-AC-A2FE-01                                      No   \n",
       "TCGA-B6-A0WS-01                                      No   \n",
       "\n",
       "                TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR  \\\n",
       "SAMPLE_ID                                                   \n",
       "TCGA-A8-A08H-01                                       Yes   \n",
       "TCGA-A7-A0CD-01                                        No   \n",
       "TCGA-E9-A22B-01                                        No   \n",
       "TCGA-AR-A5QP-01                                       Yes   \n",
       "TCGA-A1-A0SN-01                                       Yes   \n",
       "...                                                   ...   \n",
       "TCGA-EW-A1P8-01                                       Yes   \n",
       "TCGA-BH-A1F5-01                                       Yes   \n",
       "TCGA-LL-A73Z-01                                        No   \n",
       "TCGA-AC-A2FE-01                                       Yes   \n",
       "TCGA-B6-A0WS-01                                       Yes   \n",
       "\n",
       "                TISSUE_SOURCE_SITE_CODE TUMOR_TISSUE_SITE  ...   OS_STATUS  \\\n",
       "SAMPLE_ID                                                  ...               \n",
       "TCGA-A8-A08H-01                      A8            Breast  ...    0:LIVING   \n",
       "TCGA-A7-A0CD-01                      A7            Breast  ...    0:LIVING   \n",
       "TCGA-E9-A22B-01                      E9            Breast  ...    0:LIVING   \n",
       "TCGA-AR-A5QP-01                      AR            Breast  ...    0:LIVING   \n",
       "TCGA-A1-A0SN-01                      A1            Breast  ...    0:LIVING   \n",
       "...                                 ...               ...  ...         ...   \n",
       "TCGA-EW-A1P8-01                      EW            Breast  ...  1:DECEASED   \n",
       "TCGA-BH-A1F5-01                      BH            Breast  ...  1:DECEASED   \n",
       "TCGA-LL-A73Z-01                      LL            Breast  ...  1:DECEASED   \n",
       "TCGA-AC-A2FE-01                      AC            Breast  ...  1:DECEASED   \n",
       "TCGA-B6-A0WS-01                      B6            Breast  ...  1:DECEASED   \n",
       "\n",
       "                 OS_MONTHS                  DSS_STATUS  DSS_MONTHS  \\\n",
       "SAMPLE_ID                                                            \n",
       "TCGA-A8-A08H-01   0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A7-A0CD-01  38.300950  0:ALIVE OR DEAD TUMOR FREE   38.300950   \n",
       "TCGA-E9-A22B-01  38.366703  0:ALIVE OR DEAD TUMOR FREE   38.366703   \n",
       "TCGA-AR-A5QP-01  38.958477  0:ALIVE OR DEAD TUMOR FREE   38.958477   \n",
       "TCGA-A1-A0SN-01  39.320117  0:ALIVE OR DEAD TUMOR FREE   39.320117   \n",
       "...                    ...                         ...         ...   \n",
       "TCGA-EW-A1P8-01   7.857448           1:DEAD WITH TUMOR    7.857448   \n",
       "TCGA-BH-A1F5-01  89.160667  0:ALIVE OR DEAD TUMOR FREE   89.160667   \n",
       "TCGA-LL-A73Z-01   7.462932           1:DEAD WITH TUMOR    7.462932   \n",
       "TCGA-AC-A2FE-01  86.662064           1:DEAD WITH TUMOR   86.662064   \n",
       "TCGA-B6-A0WS-01  97.478384  0:ALIVE OR DEAD TUMOR FREE   97.478384   \n",
       "\n",
       "                            DFS_STATUS  DFS_MONTHS     PFS_STATUS PFS_MONTHS  \\\n",
       "SAMPLE_ID                                                                      \n",
       "TCGA-A8-A08H-01          0:DiseaseFree    0.000000     0:CENSORED   0.000000   \n",
       "TCGA-A7-A0CD-01          0:DiseaseFree   38.300950     0:CENSORED  38.300950   \n",
       "TCGA-E9-A22B-01          0:DiseaseFree   38.366703     0:CENSORED  38.366703   \n",
       "TCGA-AR-A5QP-01                    NaN         NaN     0:CENSORED  38.958477   \n",
       "TCGA-A1-A0SN-01          0:DiseaseFree   39.320117     0:CENSORED  39.320117   \n",
       "...                                ...         ...            ...        ...   \n",
       "TCGA-EW-A1P8-01  1:Recurred/Progressed    6.805405  1:PROGRESSION   6.805405   \n",
       "TCGA-BH-A1F5-01                    NaN         NaN     0:CENSORED  89.160667   \n",
       "TCGA-LL-A73Z-01                    NaN         NaN  1:PROGRESSION   6.312260   \n",
       "TCGA-AC-A2FE-01  1:Recurred/Progressed   25.183286  1:PROGRESSION  25.183286   \n",
       "TCGA-B6-A0WS-01                    NaN         NaN     0:CENSORED  97.478384   \n",
       "\n",
       "                     OS_DAYS  isMPS  \n",
       "SAMPLE_ID                            \n",
       "TCGA-A8-A08H-01     0.000000  False  \n",
       "TCGA-A7-A0CD-01  1165.766019  False  \n",
       "TCGA-E9-A22B-01  1167.767334  False  \n",
       "TCGA-AR-A5QP-01  1185.779170  False  \n",
       "TCGA-A1-A0SN-01  1196.786402  False  \n",
       "...                      ...    ...  \n",
       "TCGA-EW-A1P8-01   239.157149  False  \n",
       "TCGA-BH-A1F5-01  2713.783213  False  \n",
       "TCGA-LL-A73Z-01   227.149259  False  \n",
       "TCGA-AC-A2FE-01  2637.733241  False  \n",
       "TCGA-B6-A0WS-01  2966.949568  False  \n",
       "\n",
       "[824 rows x 55 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_data_2_pos.sort_values('OS_STATUS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PATIENT_ID</th>\n",
       "      <th>ONCOTREE_CODE</th>\n",
       "      <th>CANCER_TYPE</th>\n",
       "      <th>CANCER_TYPE_DETAILED</th>\n",
       "      <th>TUMOR_TYPE</th>\n",
       "      <th>GRADE</th>\n",
       "      <th>TISSUE_PROSPECTIVE_COLLECTION_INDICATOR</th>\n",
       "      <th>TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR</th>\n",
       "      <th>TISSUE_SOURCE_SITE_CODE</th>\n",
       "      <th>TUMOR_TISSUE_SITE</th>\n",
       "      <th>...</th>\n",
       "      <th>OS_STATUS</th>\n",
       "      <th>OS_MONTHS</th>\n",
       "      <th>DSS_STATUS</th>\n",
       "      <th>DSS_MONTHS</th>\n",
       "      <th>DFS_STATUS</th>\n",
       "      <th>DFS_MONTHS</th>\n",
       "      <th>PFS_STATUS</th>\n",
       "      <th>PFS_MONTHS</th>\n",
       "      <th>OS_DAYS</th>\n",
       "      <th>isMPS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMPLE_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A08H-01</th>\n",
       "      <td>TCGA-A8-A08H</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A090-01</th>\n",
       "      <td>TCGA-A8-A090</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A09Z-01</th>\n",
       "      <td>TCGA-A8-A09Z</td>\n",
       "      <td>ILC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Lobular Carcinoma</td>\n",
       "      <td>Infiltrating Lobular Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A083-01</th>\n",
       "      <td>TCGA-A8-A083</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-A8-A096-01</th>\n",
       "      <td>TCGA-A8-A096</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>A8</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0X1-01</th>\n",
       "      <td>TCGA-B6-A0X1</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>1:DECEASED</td>\n",
       "      <td>245.093205</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>245.093205</td>\n",
       "      <td>1:Recurred/Progressed</td>\n",
       "      <td>16.109413</td>\n",
       "      <td>1:PROGRESSION</td>\n",
       "      <td>16.109413</td>\n",
       "      <td>7459.901865</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0RN-01</th>\n",
       "      <td>TCGA-B6-A0RN</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>263.273827</td>\n",
       "      <td>8013.265475</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0IA-01</th>\n",
       "      <td>TCGA-B6-A0IA</td>\n",
       "      <td>BRCNOS</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Carcinoma (NOS)</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>275.865470</td>\n",
       "      <td>8396.517310</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0I5-01</th>\n",
       "      <td>TCGA-B6-A0I5</td>\n",
       "      <td>IDC</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Ductal Carcinoma</td>\n",
       "      <td>Infiltrating Ductal Carcinoma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>0:DiseaseFree</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>0:CENSORED</td>\n",
       "      <td>281.290068</td>\n",
       "      <td>8561.625803</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-B6-A0RU-01</th>\n",
       "      <td>TCGA-B6-A0RU</td>\n",
       "      <td>BRCNOS</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Breast Invasive Carcinoma (NOS)</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>B6</td>\n",
       "      <td>Breast</td>\n",
       "      <td>...</td>\n",
       "      <td>0:LIVING</td>\n",
       "      <td>282.901009</td>\n",
       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
       "      <td>282.901009</td>\n",
       "      <td>1:Recurred/Progressed</td>\n",
       "      <td>101.127659</td>\n",
       "      <td>1:PROGRESSION</td>\n",
       "      <td>101.127659</td>\n",
       "      <td>8610.658020</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>824 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PATIENT_ID ONCOTREE_CODE    CANCER_TYPE  \\\n",
       "SAMPLE_ID                                                    \n",
       "TCGA-A8-A08H-01  TCGA-A8-A08H           IDC  Breast Cancer   \n",
       "TCGA-A8-A090-01  TCGA-A8-A090           IDC  Breast Cancer   \n",
       "TCGA-A8-A09Z-01  TCGA-A8-A09Z           ILC  Breast Cancer   \n",
       "TCGA-A8-A083-01  TCGA-A8-A083           IDC  Breast Cancer   \n",
       "TCGA-A8-A096-01  TCGA-A8-A096           IDC  Breast Cancer   \n",
       "...                       ...           ...            ...   \n",
       "TCGA-B6-A0X1-01  TCGA-B6-A0X1           IDC  Breast Cancer   \n",
       "TCGA-B6-A0RN-01  TCGA-B6-A0RN           IDC  Breast Cancer   \n",
       "TCGA-B6-A0IA-01  TCGA-B6-A0IA        BRCNOS  Breast Cancer   \n",
       "TCGA-B6-A0I5-01  TCGA-B6-A0I5           IDC  Breast Cancer   \n",
       "TCGA-B6-A0RU-01  TCGA-B6-A0RU        BRCNOS  Breast Cancer   \n",
       "\n",
       "                              CANCER_TYPE_DETAILED  \\\n",
       "SAMPLE_ID                                            \n",
       "TCGA-A8-A08H-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-A8-A090-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-A8-A09Z-01  Breast Invasive Lobular Carcinoma   \n",
       "TCGA-A8-A083-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-A8-A096-01   Breast Invasive Ductal Carcinoma   \n",
       "...                                            ...   \n",
       "TCGA-B6-A0X1-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-B6-A0RN-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-B6-A0IA-01    Breast Invasive Carcinoma (NOS)   \n",
       "TCGA-B6-A0I5-01   Breast Invasive Ductal Carcinoma   \n",
       "TCGA-B6-A0RU-01    Breast Invasive Carcinoma (NOS)   \n",
       "\n",
       "                                     TUMOR_TYPE  GRADE  \\\n",
       "SAMPLE_ID                                                \n",
       "TCGA-A8-A08H-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-A8-A090-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-A8-A09Z-01  Infiltrating Lobular Carcinoma    NaN   \n",
       "TCGA-A8-A083-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-A8-A096-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "...                                         ...    ...   \n",
       "TCGA-B6-A0X1-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-B6-A0RN-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-B6-A0IA-01                           Other    NaN   \n",
       "TCGA-B6-A0I5-01   Infiltrating Ductal Carcinoma    NaN   \n",
       "TCGA-B6-A0RU-01                           Other    NaN   \n",
       "\n",
       "                TISSUE_PROSPECTIVE_COLLECTION_INDICATOR  \\\n",
       "SAMPLE_ID                                                 \n",
       "TCGA-A8-A08H-01                                      No   \n",
       "TCGA-A8-A090-01                                      No   \n",
       "TCGA-A8-A09Z-01                                      No   \n",
       "TCGA-A8-A083-01                                      No   \n",
       "TCGA-A8-A096-01                                      No   \n",
       "...                                                 ...   \n",
       "TCGA-B6-A0X1-01                                      No   \n",
       "TCGA-B6-A0RN-01                                      No   \n",
       "TCGA-B6-A0IA-01                                      No   \n",
       "TCGA-B6-A0I5-01                                      No   \n",
       "TCGA-B6-A0RU-01                                      No   \n",
       "\n",
       "                TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR  \\\n",
       "SAMPLE_ID                                                   \n",
       "TCGA-A8-A08H-01                                       Yes   \n",
       "TCGA-A8-A090-01                                       Yes   \n",
       "TCGA-A8-A09Z-01                                       Yes   \n",
       "TCGA-A8-A083-01                                       Yes   \n",
       "TCGA-A8-A096-01                                       Yes   \n",
       "...                                                   ...   \n",
       "TCGA-B6-A0X1-01                                       Yes   \n",
       "TCGA-B6-A0RN-01                                       Yes   \n",
       "TCGA-B6-A0IA-01                                       Yes   \n",
       "TCGA-B6-A0I5-01                                       Yes   \n",
       "TCGA-B6-A0RU-01                                       Yes   \n",
       "\n",
       "                TISSUE_SOURCE_SITE_CODE TUMOR_TISSUE_SITE  ...   OS_STATUS  \\\n",
       "SAMPLE_ID                                                  ...               \n",
       "TCGA-A8-A08H-01                      A8            Breast  ...    0:LIVING   \n",
       "TCGA-A8-A090-01                      A8            Breast  ...    0:LIVING   \n",
       "TCGA-A8-A09Z-01                      A8            Breast  ...    0:LIVING   \n",
       "TCGA-A8-A083-01                      A8            Breast  ...    0:LIVING   \n",
       "TCGA-A8-A096-01                      A8            Breast  ...    0:LIVING   \n",
       "...                                 ...               ...  ...         ...   \n",
       "TCGA-B6-A0X1-01                      B6            Breast  ...  1:DECEASED   \n",
       "TCGA-B6-A0RN-01                      B6            Breast  ...    0:LIVING   \n",
       "TCGA-B6-A0IA-01                      B6            Breast  ...    0:LIVING   \n",
       "TCGA-B6-A0I5-01                      B6            Breast  ...    0:LIVING   \n",
       "TCGA-B6-A0RU-01                      B6            Breast  ...    0:LIVING   \n",
       "\n",
       "                  OS_MONTHS                  DSS_STATUS  DSS_MONTHS  \\\n",
       "SAMPLE_ID                                                             \n",
       "TCGA-A8-A08H-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A8-A090-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A8-A09Z-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A8-A083-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "TCGA-A8-A096-01    0.000000  0:ALIVE OR DEAD TUMOR FREE    0.000000   \n",
       "...                     ...                         ...         ...   \n",
       "TCGA-B6-A0X1-01  245.093205  0:ALIVE OR DEAD TUMOR FREE  245.093205   \n",
       "TCGA-B6-A0RN-01  263.273827  0:ALIVE OR DEAD TUMOR FREE  263.273827   \n",
       "TCGA-B6-A0IA-01  275.865470  0:ALIVE OR DEAD TUMOR FREE  275.865470   \n",
       "TCGA-B6-A0I5-01  281.290068  0:ALIVE OR DEAD TUMOR FREE  281.290068   \n",
       "TCGA-B6-A0RU-01  282.901009  0:ALIVE OR DEAD TUMOR FREE  282.901009   \n",
       "\n",
       "                            DFS_STATUS  DFS_MONTHS     PFS_STATUS  PFS_MONTHS  \\\n",
       "SAMPLE_ID                                                                       \n",
       "TCGA-A8-A08H-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "TCGA-A8-A090-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "TCGA-A8-A09Z-01                    NaN         NaN     0:CENSORED    0.000000   \n",
       "TCGA-A8-A083-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "TCGA-A8-A096-01          0:DiseaseFree    0.000000     0:CENSORED    0.000000   \n",
       "...                                ...         ...            ...         ...   \n",
       "TCGA-B6-A0X1-01  1:Recurred/Progressed   16.109413  1:PROGRESSION   16.109413   \n",
       "TCGA-B6-A0RN-01          0:DiseaseFree  263.273827     0:CENSORED  263.273827   \n",
       "TCGA-B6-A0IA-01          0:DiseaseFree  275.865470     0:CENSORED  275.865470   \n",
       "TCGA-B6-A0I5-01          0:DiseaseFree  281.290068     0:CENSORED  281.290068   \n",
       "TCGA-B6-A0RU-01  1:Recurred/Progressed  101.127659  1:PROGRESSION  101.127659   \n",
       "\n",
       "                     OS_DAYS  isMPS  \n",
       "SAMPLE_ID                            \n",
       "TCGA-A8-A08H-01     0.000000  False  \n",
       "TCGA-A8-A090-01     0.000000  False  \n",
       "TCGA-A8-A09Z-01     0.000000  False  \n",
       "TCGA-A8-A083-01     0.000000  False  \n",
       "TCGA-A8-A096-01     0.000000  False  \n",
       "...                      ...    ...  \n",
       "TCGA-B6-A0X1-01  7459.901865  False  \n",
       "TCGA-B6-A0RN-01  8013.265475  False  \n",
       "TCGA-B6-A0IA-01  8396.517310  False  \n",
       "TCGA-B6-A0I5-01  8561.625803  False  \n",
       "TCGA-B6-A0RU-01  8610.658020  False  \n",
       "\n",
       "[824 rows x 55 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_data_2_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "kmf_pos = KaplanMeierFitter()\n",
    "kmf_neg = KaplanMeierFitter()\n",
    "kmf_pos.fit(durations = survival_data_2_pos['OS_DAYS'], event_observed = survival_data_2_pos['OS_STATUS'], label = \"MPS Positive\")\n",
    "kmf_neg.fit(durations = survival_data_2_neg['OS_DAYS'], event_observed = survival_data_2_neg['OS_STATUS'], label = 'MPS Negative')\n",
    "ax = kmf_pos.plot(show_censors = True)\n",
    "ax = kmf_neg.plot(ax = ax, show_censors = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = kmf_neg.plot(ax =ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timeline'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_ylim([0.0,1.0])\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines.datasets import load_leukemia, load_lymphoma\n",
    "from lifelines import KaplanMeierFitter\n",
    "# Load datasets\n",
    "df_leukemia = load_leukemia()\n",
    "df_lymphoma = load_lymphoma()\n",
    "\n",
    "# Fit & plot leukemia dataset\n",
    "kmf_leukemia = KaplanMeierFitter()\n",
    "kmf_leukemia.fit(df_leukemia['t'], df_leukemia['Rx'], label=\"Leukemia\")\n",
    "ax = kmf_leukemia.plot()\n",
    "\n",
    "# Fit & plot lymphoma dataset\n",
    "kmf_lymphoma = KaplanMeierFitter()\n",
    "kmf_lymphoma.fit(df_lymphoma['Time'], df_lymphoma['Censor'], label=\"Lymphoma\")\n",
    "ax = kmf_lymphoma.plot(ax=ax)\n",
    "\n",
    "# Set Y axis to fixed scale\n",
    "ax.set_ylim([0.0, 1.0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cmapPy3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}